April  2017, 14(2): 491-509. doi: 10.3934/mbe.2017030

Modelling chemistry and biology after implantation of a drug-eluting stent. Part Ⅰ: Drug transport

1. 

Mathematics Applications Consortium for Science and Industry, University of Limerick, Castletroy, Co. Limerick, Ireland

2. 

Department of Mathematics, University of Portsmouth, Winston Churchill Ave, Portsmouth PO1 2UP, United Kingdom

3. 

College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter, Devon, EX4 4QF, United Kingdom

4. 

School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, University Road, Galway, Ireland

Received  April 15, 2015 Accepted  July 26, 2016 Published  October 2016

Drug-eluting stents have been used widely to prevent restenosis of arteries following percutaneous balloon angioplasty. Mathematical modelling plays an important role in optimising the design of these stents to maximise their efficiency. When designing a drug-eluting stent system, we expect to have a sufficient amount of drug being released into the artery wall for a sufficient period to prevent restenosis. In this paper, a simple model is considered to provide an elementary description of drug release into artery tissue from an implanted stent. From the model, we identified a parameter regime to optimise the system when preparing the polymer coating. The model provides some useful order of magnitude estimates for the key quantities of interest. From the model, we can identify the time scales over which the drug traverses the artery wall and empties from the polymer coating, as well as obtain approximate formulae for the total amount of drug in the artery tissue and the fraction of drug that has released from the polymer. The model was evaluated by comparing to in-vivo experimental data and good agreement was found.

Citation: Tuoi Vo, William Lee, Adam Peddle, Martin Meere. Modelling chemistry and biology after implantation of a drug-eluting stent. Part Ⅰ: Drug transport. Mathematical Biosciences & Engineering, 2017, 14 (2) : 491-509. doi: 10.3934/mbe.2017030
References:
[1]

J. P. AlberdingA. L. BaldwinJ. K. Barton and E. Wiley, Effects of pulsation frequency and endothelial integrity on enhanced arterial transmural filtration produced by pulsatile pressure, Am. J. Physiol. Heart Circ. Physiol., 289 (2005), H931-H937. doi: 10.1152/ajpheart.00775.2004. Google Scholar

[2]

A. L. BaldwinL. M. WilsonI. Gradus-PizloR. Wilensky and K. March, Effect of atherosclerosis on transmural convection and arterial ultrastructure, JArterioscler. Thromb. Vasc. Biol., 17 (1997), 3365-3375. Google Scholar

[3]

J. Bennett and C. Dubois, A novel platinum chromium everolimus-eluting stent for the treatment of coronary artery disease, Biologics: Targets and Therapy, 17 (2013), 149-159. Google Scholar

[4]

P. BiscariS. MinisiniD. PierottiG. Verzini and P. Zunino, Controlled release with finite dissolution rate, SIAM Journal on Applied Mathematics, 71 (2011), 731-752. doi: 10.1137/100790288. Google Scholar

[5]

A. BorghiE. FoaR. BalossinoF. Migliavacca and G. Dubini, Modelling drug elution from stents: Effects of reversible binding in the vascular wall and degradable polymeric matrix, Computer Methods in Biomechanics and Biomedical Engineering, 11 (2008), 367-377. doi: 10.1080/10255840801887555. Google Scholar

[6]

F. BozsakJ. Chomaz and A. I. Barakat, Modeling the transport of drugs eluted from stents: Physical phenomena driving drug distribution in the arterial wall, Biomech Model Mechanobiol, 13 (2014), 327-347. doi: 10.1007/s10237-013-0546-4. Google Scholar

[7]

D. CapodannoF. Dipasqua and C. Tamburino, Novel drug-eluting stents in the treatment of de novo coronary lesions, Vasc Health Risk Management, 7 (2011), 103-118. Google Scholar

[8]

D. S. Cohen and T. Erneux, Controlled drug release asymptotics, SIAM Journal on Applied Mathematics, 58 (1998), 1193-1204. doi: 10.1137/S0036139995293269. Google Scholar

[9]

C. ConwayJ. P. McGarry and P. E. McHugh, Modelling of atherosclerotic plaque for use in a computational test-bed for stent angioplasty, Annals of Biomedical Engineering, 42 (2014), 2425-2439. doi: 10.1007/s10439-014-1107-4. Google Scholar

[10]

G. Frenning, Theoretical analysis of the release of slowly dissolving drugs from spherical matrix systems, Journal of Controlled Release, 95 (2004), 109-117. doi: 10.1016/j.jconrel.2003.11.010. Google Scholar

[11]

M. J. LeverJ. M. Tarbell and C. G. Caro, The effect of luminal flow in rabbit carotid artery on transmural fluid transport, Experimental Physiology, 77 (1992), 553-563. doi: 10.1113/expphysiol.1992.sp003619. Google Scholar

[12]

A. D. LevinN. VukmirovicC. Hwang and E. R. Edelman, Specific binding to intracellular proteins determines arterial transport properties for rapamycin and paclitaxel, PNAS, 101 (2004), 9463-9467. doi: 10.1073/pnas.0400918101. Google Scholar

[13]

M. A. Lovich and E. R. Edelman, Computational simulations of local vascular heparin deposition and distribution, American Journal of Physiology, 271 (1996), H2014-H2024. Google Scholar

[14]

D. M. Martin and F. J. Boyle, Drug-eluting stents for coronary artery disease: A review, Medical Engineering & Physics, 33 (2011), 148-163. Google Scholar

[15]

S. McGinty, A decade of modelling drug release from arterial stents, Mathematical Bioscience, 257 (2014), 80-90. doi: 10.1016/j.mbs.2014.06.016. Google Scholar

[16]

S. McGintyS. McKeeC. McCormick and M. Wheel, Release mechanism and parameter estimation in drug-eluting stent systems: Analytical solutions of drug release and tissue transport, Mathematical Medicine and Bilology, 32 (2015), 163-186. doi: 10.1093/imammb/dqt025. Google Scholar

[17]

S. McGintyS. McKeeR. M. Wadsworth and C. McCormick, Modelling drug-eluting stents, Mathematical Medicine and Bilology, 28 (2011), 1-29. doi: 10.1093/imammb/dqq003. Google Scholar

[18]

S. McGinty and G. Pontrelli, A general model of coupled drug release and tissue absorption for drug delivery devices, Journal of Controlled Release, 217 (2015), 327-336. doi: 10.1016/j.jconrel.2015.09.025. Google Scholar

[19]

A. Peddle, T. T. N. Vo and W. Lee, Modelling chemistry and biology after implantation of a drug-eluting stent. Part Ⅱ: Cell proliferation, in progress.Google Scholar

[20]

L. E. L. PerkinsK. H. Boeke-PurkisQ. WangS. K. Stringer and L. A. Coleman, XIENCE V Everolimus-eluting coronary stent system: A preclinical assessment, Journal of Interventional Cardiology, 22 (2009), S28-S40. doi: 10.1111/j.1540-8183.2009.00451.x. Google Scholar

[21]

D. V. SakharovL. V. Kalachev and D. C. Rijken, Numerical simulation of local pharmacokinetics of a drug after intravascular delivery with an eluting stent, Journal of Drug Targeting, 10 (2002), 507-513. doi: 10.1080/1061186021000038382. Google Scholar

[22]

R. W. SirianniE. JangK. M. Miller and W. M. Saltzman, Parameter estimation methodology in a model of hydrophobic drug release from a polymer coating, Journal of Controlled Release, 142 (2010), 474-482. doi: 10.1016/j.jconrel.2009.11.021. Google Scholar

[23]

A. Tedgui and M. J. Lever, Filtration through damaged and undamaged rabbit thoracic aorta, Am. J. Physiol., 247 (1984), H784-H791. Google Scholar

[24]

A. R. TzafririA. GroothuisG. S. Price and E. R. Edelman, Stent elution rate determines drug deposition and receptor-mediated effects, Journal of Controlled Release, 161 (2012), 918-926. doi: 10.1016/j.jconrel.2012.05.039. Google Scholar

[25]

A. R. TzafririA. D. Levin and E. R. Edelman, Diffusion-limited binding explains binary dose response for local arterial and tumor drug delivery, Cell Proliferation, 42 (2009), 348-363. Google Scholar

[26]

T. T. N. VoR. YangY. Rochev and M. Meere, A mathematical model for drug delivery, Progress in Industrial Mathematics at ECMI 2010, Mathematics in Industry, 17 (2012), 521-528. Google Scholar

[27]

T. T. N. Vo, Mathematical Analysis of Some Models for Drug Delivery PhD thesis, National University of Ireland Galway, 2012.Google Scholar

[28]

P. Zunino, Multidimensional pharmacokinetic models applied to the design of drug-eluting stents, Cardiovascular Engineering, 4 (2004), 181-191. doi: 10.1023/B:CARE.0000031547.39178.cb. Google Scholar

show all references

References:
[1]

J. P. AlberdingA. L. BaldwinJ. K. Barton and E. Wiley, Effects of pulsation frequency and endothelial integrity on enhanced arterial transmural filtration produced by pulsatile pressure, Am. J. Physiol. Heart Circ. Physiol., 289 (2005), H931-H937. doi: 10.1152/ajpheart.00775.2004. Google Scholar

[2]

A. L. BaldwinL. M. WilsonI. Gradus-PizloR. Wilensky and K. March, Effect of atherosclerosis on transmural convection and arterial ultrastructure, JArterioscler. Thromb. Vasc. Biol., 17 (1997), 3365-3375. Google Scholar

[3]

J. Bennett and C. Dubois, A novel platinum chromium everolimus-eluting stent for the treatment of coronary artery disease, Biologics: Targets and Therapy, 17 (2013), 149-159. Google Scholar

[4]

P. BiscariS. MinisiniD. PierottiG. Verzini and P. Zunino, Controlled release with finite dissolution rate, SIAM Journal on Applied Mathematics, 71 (2011), 731-752. doi: 10.1137/100790288. Google Scholar

[5]

A. BorghiE. FoaR. BalossinoF. Migliavacca and G. Dubini, Modelling drug elution from stents: Effects of reversible binding in the vascular wall and degradable polymeric matrix, Computer Methods in Biomechanics and Biomedical Engineering, 11 (2008), 367-377. doi: 10.1080/10255840801887555. Google Scholar

[6]

F. BozsakJ. Chomaz and A. I. Barakat, Modeling the transport of drugs eluted from stents: Physical phenomena driving drug distribution in the arterial wall, Biomech Model Mechanobiol, 13 (2014), 327-347. doi: 10.1007/s10237-013-0546-4. Google Scholar

[7]

D. CapodannoF. Dipasqua and C. Tamburino, Novel drug-eluting stents in the treatment of de novo coronary lesions, Vasc Health Risk Management, 7 (2011), 103-118. Google Scholar

[8]

D. S. Cohen and T. Erneux, Controlled drug release asymptotics, SIAM Journal on Applied Mathematics, 58 (1998), 1193-1204. doi: 10.1137/S0036139995293269. Google Scholar

[9]

C. ConwayJ. P. McGarry and P. E. McHugh, Modelling of atherosclerotic plaque for use in a computational test-bed for stent angioplasty, Annals of Biomedical Engineering, 42 (2014), 2425-2439. doi: 10.1007/s10439-014-1107-4. Google Scholar

[10]

G. Frenning, Theoretical analysis of the release of slowly dissolving drugs from spherical matrix systems, Journal of Controlled Release, 95 (2004), 109-117. doi: 10.1016/j.jconrel.2003.11.010. Google Scholar

[11]

M. J. LeverJ. M. Tarbell and C. G. Caro, The effect of luminal flow in rabbit carotid artery on transmural fluid transport, Experimental Physiology, 77 (1992), 553-563. doi: 10.1113/expphysiol.1992.sp003619. Google Scholar

[12]

A. D. LevinN. VukmirovicC. Hwang and E. R. Edelman, Specific binding to intracellular proteins determines arterial transport properties for rapamycin and paclitaxel, PNAS, 101 (2004), 9463-9467. doi: 10.1073/pnas.0400918101. Google Scholar

[13]

M. A. Lovich and E. R. Edelman, Computational simulations of local vascular heparin deposition and distribution, American Journal of Physiology, 271 (1996), H2014-H2024. Google Scholar

[14]

D. M. Martin and F. J. Boyle, Drug-eluting stents for coronary artery disease: A review, Medical Engineering & Physics, 33 (2011), 148-163. Google Scholar

[15]

S. McGinty, A decade of modelling drug release from arterial stents, Mathematical Bioscience, 257 (2014), 80-90. doi: 10.1016/j.mbs.2014.06.016. Google Scholar

[16]

S. McGintyS. McKeeC. McCormick and M. Wheel, Release mechanism and parameter estimation in drug-eluting stent systems: Analytical solutions of drug release and tissue transport, Mathematical Medicine and Bilology, 32 (2015), 163-186. doi: 10.1093/imammb/dqt025. Google Scholar

[17]

S. McGintyS. McKeeR. M. Wadsworth and C. McCormick, Modelling drug-eluting stents, Mathematical Medicine and Bilology, 28 (2011), 1-29. doi: 10.1093/imammb/dqq003. Google Scholar

[18]

S. McGinty and G. Pontrelli, A general model of coupled drug release and tissue absorption for drug delivery devices, Journal of Controlled Release, 217 (2015), 327-336. doi: 10.1016/j.jconrel.2015.09.025. Google Scholar

[19]

A. Peddle, T. T. N. Vo and W. Lee, Modelling chemistry and biology after implantation of a drug-eluting stent. Part Ⅱ: Cell proliferation, in progress.Google Scholar

[20]

L. E. L. PerkinsK. H. Boeke-PurkisQ. WangS. K. Stringer and L. A. Coleman, XIENCE V Everolimus-eluting coronary stent system: A preclinical assessment, Journal of Interventional Cardiology, 22 (2009), S28-S40. doi: 10.1111/j.1540-8183.2009.00451.x. Google Scholar

[21]

D. V. SakharovL. V. Kalachev and D. C. Rijken, Numerical simulation of local pharmacokinetics of a drug after intravascular delivery with an eluting stent, Journal of Drug Targeting, 10 (2002), 507-513. doi: 10.1080/1061186021000038382. Google Scholar

[22]

R. W. SirianniE. JangK. M. Miller and W. M. Saltzman, Parameter estimation methodology in a model of hydrophobic drug release from a polymer coating, Journal of Controlled Release, 142 (2010), 474-482. doi: 10.1016/j.jconrel.2009.11.021. Google Scholar

[23]

A. Tedgui and M. J. Lever, Filtration through damaged and undamaged rabbit thoracic aorta, Am. J. Physiol., 247 (1984), H784-H791. Google Scholar

[24]

A. R. TzafririA. GroothuisG. S. Price and E. R. Edelman, Stent elution rate determines drug deposition and receptor-mediated effects, Journal of Controlled Release, 161 (2012), 918-926. doi: 10.1016/j.jconrel.2012.05.039. Google Scholar

[25]

A. R. TzafririA. D. Levin and E. R. Edelman, Diffusion-limited binding explains binary dose response for local arterial and tumor drug delivery, Cell Proliferation, 42 (2009), 348-363. Google Scholar

[26]

T. T. N. VoR. YangY. Rochev and M. Meere, A mathematical model for drug delivery, Progress in Industrial Mathematics at ECMI 2010, Mathematics in Industry, 17 (2012), 521-528. Google Scholar

[27]

T. T. N. Vo, Mathematical Analysis of Some Models for Drug Delivery PhD thesis, National University of Ireland Galway, 2012.Google Scholar

[28]

P. Zunino, Multidimensional pharmacokinetic models applied to the design of drug-eluting stents, Cardiovascular Engineering, 4 (2004), 181-191. doi: 10.1023/B:CARE.0000031547.39178.cb. Google Scholar

Figure 1.  The deployment of a drug-eluting stent in a diseased coronary artery. The stent is coated with a drug-loaded polymer, and subsequent to deployment of the stent, drug releases from the stent coating into the artery wall to prevent re-blockage due to restenosis
Figure 2.  Drug diffuses from a polymer coating into the artery wall. In the arterial tissue, drug molecules can associate with and dissociate from specific binding sites, and can diffuse in their free form. Drug molecules may also be convected by the outward movement of plasma through the artery wall.
Figure 3.  Numerical solutions to the initial boundary value problem (5-7) for various dimensional times $t=O(L_a^2/D_a)$, the diffusion time scale for free drug in the artery wall. The drug penetrates the artery wall on this time scale and this is evident in the figures. We have plotted profiles for the bound drug in the artery wall in (a), and the free drug in the polymer and the artery wall in (b). The parameter values used are $L=60, \varepsilon=10^{-6}$, $\eta = 0.002, K_b = 1700$, and $Pe=0.2$
Figure 4.  Numerical solutions to the initial boundary value problem (5-7) for various dimensional times $t=O(L_p^2/D_p)$. On this time scale, the behaviour in the arterial tissue is quasistatic, and is driven temporally by the decreasing flux of drug from the stent coating. We have plotted profiles for the bound drug in the artery wall in (a), and the free drug in the polymer and the artery wall in (b). The parameter values used are $L=60, \varepsilon=10^{-6}$, $\eta = 0.002, K_b = 1700$, and $Pe=0.2$
Figure 5.  Plots of the average bound drug, $m_a(t)$, in the artery wall as a function of time $t$ for various values of $\eta$, and with $L=60$, $\varepsilon=10^{-6}, K_b = 1700$, and $Pe=0.2$. On the vertical scale, $1$ corresponds to full occupancy of the specific binding sites. The rapid rise in the profiles near $t=0$ corresponds to the short time scale over which free drug in the tissue crosses the artery wall. In (a), $m_a(t)$ was calculated from numerical solutions of the initial boundary value problem (5-7). It is seen that there is significant occupancy of the binding sites for a period of a few months subsequent to the stent being implanted. In (b), we compare the asymptotic solution (28) to numerical results for the first month. The sub window shows the relative errors between the asymptotic and numerical solutions, $(m_{a,\text{numerical}}-m_{a,\text{asymptotic}})/m_{a,\text{numerical}}$
Figure 6.  Comparison between in-vivo experimental data and modelling profiles for the fraction of the total drug released. In (a), Rapamycin release from Cypher [24] with $D_p=1.2 \times 10^{-10}$ mm$^2$/s and the mean squared error MSE $= 9 \times 10^{-4}$. In (b), Everolimus release from Xience V [20] with $D_p= 1.1 \times 10^{-11}$ mm$^2$/s and MSE $= 9 \times 10^{-3}$
Figure 7.  Comparison of the average mass of drug in the artery wall calculated by (30) to in-vivo experimental data after (a) implantation of Cypher stents from 1 to 30 days [24] and (b) implantation of Xience V stents from 1 to 120 days [20]. Values have been normalized with respect to the initial drug content, $M(0)$. The parameter values used are: $L_a=0.45$ mm, $D_a=2.5 \times 10^{-4}$ mm$^2$/s, $V_a=5.8 \times 10^{-5}$ mm/s, $K_b = 300$; (a) $L_p=1.26 \times 10^{-2}$ mm, $M(0)= 174.89$ $\mu$g, $D_p=6.0 \times 10^{-11}$ mm$^2$/s, $\eta = 0.0075$ (MSE=$1.7 \times 10^{-4}$); and (b) $L_p= 7.6 \times 10^{-3}$ mm, $M(0)= 100$ $\mu$g, $D_p=1.0 \times 10^{-11}$ mm$^2$/s, $\eta = 0.002$ (MSE=$2.2 \times 10^{-4}$)
Table 1.  Data for some commercial drug-eluting stents
DES/DrugPolymer thickness ($L_p$)Drug doseLife timeReferences
Cypher/Rapamycin12.6μm140 μg/cm2 stent surface area80% of drug released within 30 days[7,14]
Taxus/Paclitaxel16 μm100 μg/cm2 stent surface areaEarly 48 hours burst, then slow release over 10 days[7,14]
Endeavor/Zotarolimus5.3 μm100 μg/cm stent length95% of drug released within 15 days[7,14
Xience V/Everolimus7.6 μm100 μg/cm2 stent surface area80% of drug released within 30 days[7,14]
Promus Element/Everolimus7 μm100 μg/cm2 stent surface area80% of drug released within 30 days[3]
DES/DrugPolymer thickness ($L_p$)Drug doseLife timeReferences
Cypher/Rapamycin12.6μm140 μg/cm2 stent surface area80% of drug released within 30 days[7,14]
Taxus/Paclitaxel16 μm100 μg/cm2 stent surface areaEarly 48 hours burst, then slow release over 10 days[7,14]
Endeavor/Zotarolimus5.3 μm100 μg/cm stent length95% of drug released within 15 days[7,14
Xience V/Everolimus7.6 μm100 μg/cm2 stent surface area80% of drug released within 30 days[7,14]
Promus Element/Everolimus7 μm100 μg/cm2 stent surface area80% of drug released within 30 days[3]
Table 2.  Drug diffusivities, $D_a$, in arterial tissues
DrugDiffusivity $D_a$ (mm2/s)References
Rapamycin $1.5 - 2.5 \times 10^{-4}$[24]
Paclitaxel $2.6 \times 10^{-6}$[28]
Dextran $3.0 \times 10^{-5}$[12]
Heparin $7.7 \times 10^{-6}$[13]
DrugDiffusivity $D_a$ (mm2/s)References
Rapamycin $1.5 - 2.5 \times 10^{-4}$[24]
Paclitaxel $2.6 \times 10^{-6}$[28]
Dextran $3.0 \times 10^{-5}$[12]
Heparin $7.7 \times 10^{-6}$[13]
Table 3.  Data for drug diffusivities in polymers. Note: PEVA = Poly(ethylene-co-vinyl acetate), PBMA = Poly(n-butyl methacrylate), PVDF-HFP = Poly(vinylidene fluoride-co-hexafluoropropylene), SIBS = Poly(styrene-b-isobutylene-b-styrene)
DrugDiffusivity $D_p$ (mm2/s)PolymerDESReferences
Rapamycin $1.2 \times 10^{-10}$PEVA and PBMACypherThis study
$6.3 \times 10^{-11}$PEVA and PBMACypher[16]
Everolimus $1.0 - 1.2 \times 10^{-11}$PBMA and PVDF-HFPXience VThis study
Paclitaxel $O(10^{-15}) - O(10^{-11})$SIBSTaxus[22]
DrugDiffusivity $D_p$ (mm2/s)PolymerDESReferences
Rapamycin $1.2 \times 10^{-10}$PEVA and PBMACypherThis study
$6.3 \times 10^{-11}$PEVA and PBMACypher[16]
Everolimus $1.0 - 1.2 \times 10^{-11}$PBMA and PVDF-HFPXience VThis study
Paclitaxel $O(10^{-15}) - O(10^{-11})$SIBSTaxus[22]
Table 4.  Data for transmural velocities and pressures in the arterial wall
ArteryTransmural velocity ($\times 10^{-5}$ mm/s)Transmural pressure (mmHg)References
Porcine coronary5.850[24]
Rabbit carotid1.85±0.33110[11]
8.9±6.860[1]
Rabbit thoracic aorta2.8±0.970[23]
4.4±1.4180[23]
Rabbit femoral artery3.3±1.330[2]
8.1±2.460[2]
9.9±2.590[2]
ArteryTransmural velocity ($\times 10^{-5}$ mm/s)Transmural pressure (mmHg)References
Porcine coronary5.850[24]
Rabbit carotid1.85±0.33110[11]
8.9±6.860[1]
Rabbit thoracic aorta2.8±0.970[23]
4.4±1.4180[23]
Rabbit femoral artery3.3±1.330[2]
8.1±2.460[2]
9.9±2.590[2]
Table 5.  Values for some of the non-dimensional parameters appearing in the model for some commercial drug/stent systems. For the purposes of calculation, the values $L_a=0.75$ mm and $V_a=6 \times 10^{-5}$ mm/s [24] have been chosen. For Rapamycin, $k_{\rm {off}} = 0.096$ min$^{-1}$, $k_{\rm {on}} = 4.8 \times 10^{7}$ M$^{-1}$ min$^{-1}$, and $b^*= 3.3 \times 10^{-6}$ M [27,24]. For Paclitaxel, $k_{\rm {off}} = 5.46$ min$^{-1}$, $k_{\rm {on}} = 2.2 \times 10^{8}$ M$^{-1}$ min$^{-1}$, and $b^*= 1.0 \times 10^{-5}$ M [27,28]. The remaining values used can be found in Tables 1 and 2
Stent/Drug $L=L_a/L_p$ $Pe=V_aL_a/D_a$ $K_b=k_{\rm {on}} b^*/k_{\rm {off}}$
Cypher/Rapamycin600.21700
Taxus/Paclitaxel4717400
Endeavor/Zotarolimus1410.21700
Xience V/Everolimus990.21700
Promus Element/Everolimus1070.21700
Stent/Drug $L=L_a/L_p$ $Pe=V_aL_a/D_a$ $K_b=k_{\rm {on}} b^*/k_{\rm {off}}$
Cypher/Rapamycin600.21700
Taxus/Paclitaxel4717400
Endeavor/Zotarolimus1410.21700
Xience V/Everolimus990.21700
Promus Element/Everolimus1070.21700
[1]

Adam Peddle, William Lee, Tuoi Vo. Modelling chemistry and biology after implantation of a drug-eluting stent. Part Ⅱ: Cell proliferation. Mathematical Biosciences & Engineering, 2018, 15 (5) : 1117-1135. doi: 10.3934/mbe.2018050

[2]

Shalela Mohd Mahali, Song Wang, Xia Lou. Determination of effective diffusion coefficients of drug delivery devices by a state observer approach. Discrete & Continuous Dynamical Systems - B, 2011, 16 (4) : 1119-1136. doi: 10.3934/dcdsb.2011.16.1119

[3]

John Boscoh H. Njagarah, Farai Nyabadza. Modelling the role of drug barons on the prevalence of drug epidemics. Mathematical Biosciences & Engineering, 2013, 10 (3) : 843-860. doi: 10.3934/mbe.2013.10.843

[4]

Shalela Mohd--Mahali, Song Wang, Xia Lou, Sungging Pintowantoro. Numerical methods for estimating effective diffusion coefficients of three-dimensional drug delivery systems. Numerical Algebra, Control & Optimization, 2012, 2 (2) : 377-393. doi: 10.3934/naco.2012.2.377

[5]

Mario Grassi, Giuseppe Pontrelli, Luciano Teresi, Gabriele Grassi, Lorenzo Comel, Alessio Ferluga, Luigi Galasso. Novel design of drug delivery in stented arteries: A numerical comparative study. Mathematical Biosciences & Engineering, 2009, 6 (3) : 493-508. doi: 10.3934/mbe.2009.6.493

[6]

Urszula Ledzewicz, Shuo Wang, Heinz Schättler, Nicolas André, Marie Amélie Heng, Eddy Pasquier. On drug resistance and metronomic chemotherapy: A mathematical modeling and optimal control approach. Mathematical Biosciences & Engineering, 2017, 14 (1) : 217-235. doi: 10.3934/mbe.2017014

[7]

Nicolas Bacaër, Cheikh Sokhna. A reaction-diffusion system modeling the spread of resistance to an antimalarial drug. Mathematical Biosciences & Engineering, 2005, 2 (2) : 227-238. doi: 10.3934/mbe.2005.2.227

[8]

Song Wang, Xia Lou. An optimization approach to the estimation of effective drug diffusivity: From a planar disc into a finite external volume. Journal of Industrial & Management Optimization, 2009, 5 (1) : 127-140. doi: 10.3934/jimo.2009.5.127

[9]

Zhenzhen Chen, Sze-Bi Hsu, Ya-Tang Yang. The continuous morbidostat: A chemostat with controlled drug application to select for drug resistance mutants. Communications on Pure & Applied Analysis, 2020, 19 (1) : 203-220. doi: 10.3934/cpaa.2020011

[10]

Lambertus A. Peletier. Modeling drug-protein dynamics. Discrete & Continuous Dynamical Systems - S, 2012, 5 (1) : 191-207. doi: 10.3934/dcdss.2012.5.191

[11]

Urszula Ledzewicz, Heinz Schättler. Drug resistance in cancer chemotherapy as an optimal control problem. Discrete & Continuous Dynamical Systems - B, 2006, 6 (1) : 129-150. doi: 10.3934/dcdsb.2006.6.129

[12]

Brandon Lindley, Qi Wang, Tianyu Zhang. A multicomponent model for biofilm-drug interaction. Discrete & Continuous Dynamical Systems - B, 2011, 15 (2) : 417-456. doi: 10.3934/dcdsb.2011.15.417

[13]

Cristian Tomasetti, Doron Levy. An elementary approach to modeling drug resistance in cancer. Mathematical Biosciences & Engineering, 2010, 7 (4) : 905-918. doi: 10.3934/mbe.2010.7.905

[14]

Boris Baeumer, Lipika Chatterjee, Peter Hinow, Thomas Rades, Ami Radunskaya, Ian Tucker. Predicting the drug release kinetics of matrix tablets. Discrete & Continuous Dynamical Systems - B, 2009, 12 (2) : 261-277. doi: 10.3934/dcdsb.2009.12.261

[15]

Avner Friedman, Najat Ziyadi, Khalid Boushaba. A model of drug resistance with infection by health care workers. Mathematical Biosciences & Engineering, 2010, 7 (4) : 779-792. doi: 10.3934/mbe.2010.7.779

[16]

Azmy S. Ackleh, Jeremy J. Thibodeaux. Parameter estimation in a structured erythropoiesis model. Mathematical Biosciences & Engineering, 2008, 5 (4) : 601-616. doi: 10.3934/mbe.2008.5.601

[17]

Urszula Ledzewicz, Heinz Schättler, Mostafa Reisi Gahrooi, Siamak Mahmoudian Dehkordi. On the MTD paradigm and optimal control for multi-drug cancer chemotherapy. Mathematical Biosciences & Engineering, 2013, 10 (3) : 803-819. doi: 10.3934/mbe.2013.10.803

[18]

Andrzej Swierniak, Jaroslaw Smieja. Analysis and Optimization of Drug Resistant an Phase-Specific Cancer. Mathematical Biosciences & Engineering, 2005, 2 (3) : 657-670. doi: 10.3934/mbe.2005.2.657

[19]

Rebeccah E. Marsh, Jack A. Tuszyński, Michael Sawyer, Kenneth J. E. Vos. A model of competing saturable kinetic processes with application to the pharmacokinetics of the anticancer drug paclitaxel. Mathematical Biosciences & Engineering, 2011, 8 (2) : 325-354. doi: 10.3934/mbe.2011.8.325

[20]

Ami B. Shah, Katarzyna A. Rejniak, Jana L. Gevertz. Limiting the development of anti-cancer drug resistance in a spatial model of micrometastases. Mathematical Biosciences & Engineering, 2016, 13 (6) : 1185-1206. doi: 10.3934/mbe.2016038

2018 Impact Factor: 1.313

Metrics

  • PDF downloads (18)
  • HTML views (4)
  • Cited by (0)

Other articles
by authors

[Back to Top]